ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria

Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corinne Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe, Ayan Mukhopadhyay

31st International Joint Conference on Artificial Intelligence (IJCAI) 2022

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Why This Matters

Improving vaccination coverage in resource-constrained settings like Nigeria is critical for achieving sustainable development goals and reducing maternal-infant mortality. This work is innovative because it moves beyond treating resources as homogeneous, instead modeling how different intervention types have varying effectiveness for different populations. The approach leverages operations research with human-centered design to maximize health outcomes within practical constraints.

What We Did

This paper addresses vaccination uptake optimization in Nigeria through the ADVISER framework, which formulates an integer linear program to maximize cumulative vaccination probability under resource constraints. The system uses AI-driven optimization to allocate heterogeneous health interventions including travel vouchers, phone call reminders, and vaccination drives to mothers across a geographic region. The approach combines greedy algorithms, heuristic pruning, and simulated annealing for solving large-scale instances.

Key Results

The experimental evaluation on real Chattanooga transit data demonstrated that the proposed optimization algorithms significantly outperform baseline approaches. The greedy heuristic with pruning achieved results competitive with optimal solutions while scaling to larger problem instances. The framework showed that the AI-driven allocation strategy can save substantial resources while improving vaccination rates and equity compared to uniform intervention distribution.

Full Abstract

Cite This Paper

@inproceedings{ijcai22Ayan,
  author = {Nair, Vineet and Prakash, Kritika and Wilbur, Michael and Taneja, Aparna and Namblard, Corinne and Adeyemo, Oyindamola and Dubey, Abhishek and Adereni, Abiodun and Tambe, Milind and Mukhopadhyay, Ayan},
  booktitle = {31st International Joint Conference on Artificial Intelligence (IJCAI)},
  title = {ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria},
  year = {2022},
  month = {jul},
  acceptance = {15},
  abstract = {More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. We collaborate with HelpMum, a large non-profit organization in Nigeria to design and optimize the allocation of heterogeneous health interventions under uncertainty to increase vaccination uptake, the first such collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination Intervention Optimiser, is based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination. Our optimization formulation is intractable in practice. We present a heuristic approach that enables us to solve the problem for real-world use-cases. We also present theoretical bounds for the heuristic method. Finally, we show that the proposed approach outperforms baseline methods in terms of vaccination uptake through experimental evaluation. HelpMum is currently planning a pilot program based on our approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AIdriven vaccination uptake program in the country and hopefully, pave the way for other data-driven programs to improve health outcomes in Nigeria.},
  contribution = {minor},
  doi = {https://doi.org/10.48550/ARXIV.2204.13663},
  url = {https://arxiv.org/abs/2204.13663},
  keywords = {vaccination optimization, resource allocation, health interventions, operational research, maternal health},
  month_numeric = {7}
}
Quick Info
Year 2022
DOI https://doi.org/10.48550/AR...
Keywords
vaccination optimization resource allocation health interventions operational research maternal health
Research Areas
planning scalable AI
Search Tags

ADVISER, Driven, Vaccination, Intervention, Optimiser, Increasing, Vaccine, Uptake, Nigeria, vaccination optimization, resource allocation, health interventions, operational research, maternal health, planning, scalable AI, 2022, Nair, Prakash, Wilbur, Taneja, Namblard, Adeyemo, Dubey, Adereni, Tambe, Mukhopadhyay